Hello all,
I did ssGSEA on my gene expression profile and then as the ssGSEA score are so big I used ssGSEA.normalize() in multiGSEA package in r to normalize them. Then I drew boxplot using ggplot2 to see if there is significant different between my 2 groups. This is part of my data:
HALLMARK_PANCREAS group
1 0.6357577 control
2 0.6139007 control
3 0.6221403 control
4 0.8393437 hROBO
5 0.8703753 hROBO
6 0.8530723 hROBO
this is the script to draw boxplot i r:
g <- ggplot(df, aes(x = group, y = HALLMARK_PANCREAS, color = group))+geom_boxplot()+geom_quasirandom(width=.1)+ labs(y = "ssGSEA score")+theme_bw()+ggtitle("HALLMARK_PANCREAS") +theme(legend.position = "none", plot.title = element_text(hjust = 0.5), axis.title.x=element_blank())
#add p-value
add_pval(g, pairs = list(c(1, 2)), test='wilcox.test')
this is the output:
I also check other ways to see if the difference is significant. For example this one:
g+geom_signif(comparisons = list(c("control", "hROBO")),
map_signif_level=TRUE, color = "black")
this is output:
I am wondering why this difference is not significant based on wilcoxon test?! Even when I change the data and increase the difference between two group's values; still keep giving me p =0.1 !! Could anyone please help me? I am really confused. Am I doing sth wrong?
I really appreciate any help!
•
written
by
Raheleh
2 answers
Because wilcox.test requires a sample size n>10. In your case, you only have 3 samples per condition. Therefore, a t.test is more suitable.
This is true. But as igor pointed out above you need more replicates for a non-parametric test.
Log in to answer this question.
You can try running the test manually to see how the different inputs impact the results. For example:
If you add more replicates, you'll see the p-value quickly decrease.